Open AccessAutomatic Segmentation and Classification of Liver Tumor using Hybrid Neural Network
Author(s) -
Ms.A. Cibi*,
D. Ramya,
V. Ramya
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a2279.059120
Subject(s) - artificial intelligence , segmentation , computer science , liver tumor , pattern recognition (psychology) , artificial neural network , image segmentation , classifier (uml) , active contour model , computer vision , medicine , cancer research , hepatocellular carcinoma
Liver tumor is most common nowadays. Liver tumor segmentation is one of the most essential steps in treating it. We have chosen CT scan image for liver tumor diagnosis. Accurate tumor segmentation is done using computed tomography (CT) images. Since the manual identification is not that much accurate and time consuming, we go for active contour method. This automatic segmentation method is highly accurate and provides very less time for computation. The back propagation classifier method has a very good accuracy rate and a very less error rate and hence achieved the best result. The proposed method we used in this paper is back propagation classification algorithm for the detection of early and final stages of liver tumor. For the automatic segmentation, we use an active contour method to segment the liver and liver tumor to overcome the manual segmentation problem. This is an automatic method will help us to know whether the tumor is in benign or malignant stage.